Many data sources, such as commercial data repositories, utility company customer databases, etc., to list only a few examples, store data records corresponding to individual entities, such as people, companies, etc. The data records are typically comprised of multiple data elements, and the value for each data element typically represents a particular aspect of the entity's identity, or other information related to the entity. Numerous commercial and noncommercial enterprises employ such data sources in a variety of ways as an integral part of their product or service offerings and daily operations.
Unfortunately, given the potentially vast array of records a data source can include, it often proves to be a challenge to search, analyze, and/or manipulate the entity-representing data in a meaningful way. Furthermore, some data sources contain inaccurate or out-dated information. For example, even using a well-indexed data source, it often can be difficult to identify with sufficient certainty that one or more particular records actually correspond to the specific entity they putatively represent. It can also be difficult to identify associations between multiple seemingly independent entity data records. Due to variations in the type, amount, and structure of data elements each data source can employ for its respective data records, the challenges of identifying and associating individual entities can be greatly magnified if multiple data sources are employed.